AdaBoost regression uses ensemble learning to improve the performance of numeric prediction models. The video below explains how to use adaBoost with Python.

AdaBoost classification is a type of ensemble learning. What this means is that the algorithm makes multiple models that work together to make predictions. Such techniques are powerful in improving the strength of models. The video below explains how to use this algorithm within Python.

Elastic net regression has all the strengths of both ridge and lasso regression without the apparent weaknesses. As such this is a great algorithm for regularized regression. The video below explains how to use this algorithm with Python

Lasso regression is another algorithm that uses regularization to handle variables. Essentially, this algorithm will reduce coefficients to zero based on whether they contribute meaningfully to the results. The video below will explain how to use Lasso regression in Python.

Ridge regression belongs to a family of regression called regularization regression. This family of regression uses various mathematical techniques to reduce or remove coefficients from a regression model. In the case of ridge, this algorithm will reduce coefficients close to zero but never actually remove variables from a model. In ...

Hyper-parameter tuning is one way of taking your model development to the next level. This tool provides several ways to make small adjustments that can reap huge benefits. In the video below, we will look at tuning the hyper-parameters of a KNN model. Naturally, this tuning process can be used ...

Cross-validation is a valuable tool for assessing a model’s ability to generalize. In the video below, we will look at how to use cross-validation with Python.

Matplotlib is a data visualization module used often in Python. In this video, we will go over some introductory basic commands. Doing so will allow anybody who wants to be able to make simple manipulations to their visualizations.

In the video below we will take a look at how to perform a random forest regression analysis with Python. Random forest is one of many tools that can be used in the field of data science to gain insights to help people.

K nearest neighbor classification is another tool used in machine learning to predict what class an observation belongs to. In this video, we will learn how to implement this algorithm using Python.